Network Information Enhances Unreliable News Domain Detection

2026-08-03

AI Summary

This study addresses the growing challenge of fake news detection, exacerbated by generative AI and low-credibility sources mimicking legitimate media. The authors propose a novel paradigm that eschews direct content analysis, instead constructing a domain co-occurrence network from URL-sharing behaviors in Telegram chats. They uncover, for the first time, a homophily effect with respect to source reliability within this network and demonstrate that propagation topology alone can effectively assess domain credibility. Their approach integrates GraphSAGE, multilingual text embeddings, and propagation dynamics to perform reliability classification at the domain level. Experimental results show that the method achieves an accuracy of 0.53 without content features and 0.63 when content is included, yielding a relative improvement of 13–14% over non-graph baselines.

Abstract

Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag. We ask whether network structure can improve news reliability classification, taking a domain-level approach that shifts the focus from individual articles to source reliability. From URL-sharing patterns in Telegram chats, we build a statistically validated domain co-sharing network and find assortative mixing by reliability: low-reliability domains group together, as do reliable ones. Exploiting this structure, we compare Graph Neural Networks against network-unaware baselines using both content-aware features (multilingual text embeddings) and content-agnostic features (spreading dynamics). GNNs consistently outperform Multi-Layer Perceptrons on identical features, with GraphSAGE best in both settings (accuracy 0.63 with content, 0.53 without), a 13-14% relative gain over the network-unaware baseline. Network topology thus systematically improves domain reliability assessment, and remains effective even when content analysis is infeasible.

Problem

• unreliable news detection
• domain reliability
• network structure
• content-based detection
• generative AI

Innovation

• Graph Neural Networks
• domain co-sharing network
• assortative mixing
• unreliable news detection
• content-agnostic features